CS60071: Algorithms For Bioinformatics
| CS60071 | |||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Course name | Algorithms For Bioinformatics | ||||||||||||||||||||||||||
| Offered by | Computer Science & Engineering | ||||||||||||||||||||||||||
| Credits | 3 | ||||||||||||||||||||||||||
| L-T-P | 3-0-0 | ||||||||||||||||||||||||||
| Previous Year Grade Distribution | |||||||||||||||||||||||||||
| |||||||||||||||||||||||||||
| Semester | Autumn | ||||||||||||||||||||||||||
Syllabus
Syllabus mentioned in ERP
Sequence similarity, homology, and alignment. Pairwise alignment: scoring model, dynamic programmingalgorithms, heuristic alignment, and pairwise alignment using Hidden Markov Models. Multiple alignment:scoring model, local alignment gapped and ungapped global alignment. Motif finding: motif models, findingoccurrence of known sites, discovering new sites. Gene Finding: predicting reading frames, maximaldependence decomposition. Analysis of DNA microarray data using hierarchical clustering, model-basedclustering, expectation-maximization clustering, Bayesian model selection.
Concepts taught in class
Student Opinion
How to Crack the Paper
Classroom resources
Additional Resources
Time Table
| Day | 8:00-8:55 am | 9:00-9:55 am | 10:00-10:55 am | 11:00-11:55 am | 12:00-12:55 pm | 2:00-2:55 pm | 3:00-3:55 pm | 4:00-4:55 pm | 5:00-5:55 pm | |
|---|---|---|---|---|---|---|---|---|---|---|
| Monday | CSE-108 | |||||||||
| Tuesday | CSE-108 | CSE-108 | ||||||||
| Wednesday | ||||||||||
| Thursday | ||||||||||
| Friday |